Emotion based Music Recommendation System using Machine Learning

Emotion recognition, integral to fields like cognitive science and artificial intelligence, involves identifying human emotions through inputs such as facial expressions, speech patterns, and body signals. Multimodal emotion recognition enhances accuracy by integrating data from visual, audio, and textual sources, utilizing advanced neural networks to address the limitationsof single-modality systems. Music often evokes and conveys emotions, and this relationship between music and emotion is leveraged in emotion-based music recommendation systems. Such systems typically analyze the audio features of music, such as tempo, mode, harmony, and rhythm, and classify theminto different emotional categories, like happy, sad, or energetic. By recognizing a user's current emotional statethrough various means—such as analyzing their facial expressions or voice— multimodal emotion recognition systems can make personalized music recommendations that match or influence their mood.